Few-shot medical image segmentation (FSMIS) has attracted increasing attention as a promising technique for solving medical image segmentation tasks by relying on only a few labeled data from new classes. Current FSMIS methods typically employ pixel-level semantic correlations between support-query image pairs to guide the segmentation of query images. However, the class information gap between support and query images may induce severe mismatches, leading to semantic ambiguity between foreground and background pixels. To address this issue, we propose a novel mask-guided proxy mining network (MPMNet), which mines a set of representative reference features (termed proxies) from support and query images to rectify foreground-background ambiguity. Specifically, to eliminate false pairwise matches caused by excessive intra-class variations, we design a mask-guided proxy mining module to adaptively learn representative proxies that can perceive visual differences between objects with different scales and shapes. Moreover, we integrate a hierarchical prior generation module and a context-aware feature enrichment module into MPMNet to obtain multi-scale information and enhance the discriminability of features.With these well-designed components and structures, our MPMNet can effectively overcome the adverse effects of false pixel matches by establishing proxy-level semantic correlations. Extensive experiments on three standard medical segmentation benchmarks demonstrate that our MPMNet significantly outperforms previous state-of-the-art methods, with a mean gain of 2.71% in DSC across all datasets. The code is available at: https://github.com/donglongzi/MPMNet.
Huang et al. (Thu,) studied this question.